Preparatory Study on Ecodesign and Energy Labelling of ... · FWC ENER/C3/2015-619-Lot 1 TASK 3...

August 2019

Preparatory Study on Ecodesign and Energy Labelling of Batteries under

FWC ENER/C3/2015-619-Lot 1

TASK 3 Report

Users – For Ecodesign and Energy Labelling

VITO, Fraunhofer, Viegand Maagøe

EUROPEAN COMMISSION

Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs

Study team leader:

Paul Van Tichelen

VITO/Energyville – [email protected]

Key technical experts:

Cornelius Moll

Antoine Durand

Clemens Rohde

Authors of Task 3:

Cornelius Moll - Fraunhofer ISI

Antoine Durand - Fraunhofer ISI

Clemens Rohde - Fraunhofer ISI

Quality Review:

Jan Viegand - Viegand Maagøe A/S

Project website: https://ecodesignbatteries.eu/

Version history:

Version 1: Version made available in December 2018 for the Stakeholders to comment

and discuss in the first stakeholder meeting.

Version 2: Version made available in April 2019 in preparation of the second

stakeholder meeting

- review based on the input from the stakeholder comments (some data input and

remarks on the base case selection)

- QFU formula and Application Service Energy formula improved, calculation of losses

added

- substitution of base case passenger BEV and light commercial BEV with BEV medium-

to large-sized and BEV small-sized

- adapted argumentation for base case selection

- some data assumptions, related to the bases cases were changed

- missing sections were added

Version 3: (this version) Version made available in August 2019

- consideration of feedback from the second stakeholder meeting and of written

feedback received after the second stakeholder meeting

- argumentation for selection of base cases slightly adapted (LCV represented partially

by passenger cars)

- information on potential impact of vehicle to grid services on battery added

- increase of annual cycles of residential ESS due to provision of grid services discussed

- discrepancy of battery and vehicle lifetime addressed

https://ecodesignbatteries.eu/

EUROPEAN COMMISSION

Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs

Directorate Directorate C – Industrial Transformation and Advanced Value Chains

Unit Directorate C1

Contact: Cesar Santos

E-mail: [email protected]

European Commission B-1049 Brussels

LEGAL NOTICE

This document has been prepared for the European Commission however it reflects the views only of the authors, and the

Commission cannot be held responsible for any use which may be made of the information contained therein.

More information on the European Union is available on the Internet (http://www.europa.eu).

This report has been prepared by the authors to the best of their ability and knowledge. The authors do not assume liability

for any damage, material or immaterial, that may arise from the use of the report or the information contained therein.

Luxembourg: Publications Office of the European Union, 2019

ISBN number [TO BE INCLUDED]

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Reproduction is authorised provided the source is acknowledged.

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Preparatory study on Ecodesign and Energy Labelling of batteries

6

Contents

LIST OF ABBREVIATIONS ..............................................................................................7

LIST OF FIGURES .............................................................................................................9

LIST OF TABLES .............................................................................................................11

3. TASK 3: USERS........................................................................................12

3.1. Subtask 3.1 - System aspects in the use phase affecting direct energy

consumption ...............................................................................................12

3.1.1. Strict product approach to battery systems ................................................13

3.1.2. Extended product approach ........................................................................38

3.1.3. Technical systems approach ......................................................................42

3.1.4. Functional systems approach .....................................................................42

3.2. Subtask 3.2 - System aspects in the use phase affecting indirect energy consumption ...............................................................................................44

3.3. Subtask 3.3 - End-of-Life behaviour .........................................................46

3.3.1. Product use & stock life .............................................................................51

3.3.2. Repair and maintenance practice ...............................................................52

3.3.3. Collection rates, by fraction (consumer perspective) ................................53

3.3.4. Estimated second hand use, fraction of total and estimated second

product life (in practice).............................................................................55

3.4. Subtask 3.4 - Local Infrastructure (barriers and opportunities) .................56

3.4.1. Energy: reliability, availability and nature .................................................56

3.4.2. Charging Infrastructure for EV ..................................................................56

3.4.3. Installation, e.g. availability and level of know-how .................................56

3.4.4. Lack of trust in second-hand products .......................................................57

3.4.5. Availability of CE marking and producer liability in second-life applications ................................................................................................57

3.5. Subtask 3.5 – Summary of data and Recommendations ............................57

4. PUBLICATION BIBLIOGRAPHY ..........................................................60


7

List of abbreviations

Abbreviations Descriptions

AC Alternating current

Ah Ampere-hour

AS Application service energy

BEV Battery-electric vehicles

BMS Battery management system

C Capacity

Cn Rated capacity

DC Direct current

DOD Depth of Discharge

E Energy

EOL End of life

EPA Environmental Protection Agency

ERated Rated energy

ESS Energy storage system

EV Electric vehicle

FC Full cycle

FESS Flywheel energy storage systems

FTP Federal Test Procedure

GHG Greenhouse gases

GVW Gross vehicle weight

HDT Heavy-duty truck

HDTU Heavy-duty tractor unit

I Current

ICEV Internal combustion engine vehicles

It Reference test current

kWh Kilowatt hour

LCal Calendar life

LCyc Cycle life

LFP Lithium iron phosphate

LIB Lithium-ion battery

NaNiCl2 Sodium nickel chloride

NaS Sodium-sulphur

nC C-rate

NEDC New European Driving Cycle

NiMH Nickel-metal hydride batteries

NMC Nickel manganese cobalt

Pb Lead-acid

PEF Product environmental footprint

PEM-FC Proton exchange membrane fuel cell

PHEV Plug-in-hybrid-electric vehicle

PV Photovoltaic

QFU Quantity of functional units

R Internal resistance

RFB Redox-flow battery


8

RT Room temperature

SD Self-discharge

SOC State of charge

SOH State of health

SOHcap Capacity degradation

T Time

V Voltage

VKT Vehicle kilometres travelled

VL Voltage limits

VOC Open circuit voltage

VR Rated voltage

WLTP Worldwide Harmonized Light Vehicle Test Procedure

ηE Energy efficiency

ηV Voltaic efficiency


9

List of figures

Figure 1: Representation of the battery system components and their system boundaries. 13

Figure 2: Visualisation of terms related to QFU calculation. .................................................. 15

Figure 3: Typical ESS charging/discharging cycle (IEC 62933-2) ........................................ 17

Figure 4: Cycle test profile PHEV (left) and BEV (right) (discharge-rich) (ISO 12405-4) ...... 18

Figure 5: Charge and discharge voltages (left y-axis) and efficiency (right y-axis) of fresh cells

(Source: Redondo-Iglesias et al. (2018a)). .......................................................................... 20

Figure 6: Typical battery system data sheet (Source: Akasol (2018)). ................................. 22

Figure 7: GHG emissions from road transport in the EU28 in 2016 by transport mean [%]

(Source: European Commission (2018)). ............................................................................ 23

Figure 8: Sales-weighted average of xEV battery capacities for passenger cars [kWh] (Source:

ICCT (2018)) ....................................................................................................................... 25

Figure 9: Daily and single route driving distances of passenger cars in Germany (Source:

Funke (2018)). .................................................................................................................... 27

Figure 10: Battery energy efficiency losses of Nissan Leaf (2012) (Source: Lohse-Busch et al.

(2012)) ................................................................................................................................ 28

Figure 11: Comparison of speed profiles for WLTP and NEDC (Source: VDA (2018)) ........ 29

Figure 12: Speed profile of EPA Federal Test Procedure (Source: EPA (2018)). ................ 30

Figure 13: Current change curves (Source: Xu et al. (2017)) .............................................. 31

Figure 14: Household load profile of PV with and without battery (Source:(SMA 2014) SMA

(2014) ) ............................................................................................................................... 35

Figure 15: Load profile of commercial ESS (source: Hornsdale Power Reserve (2018)) ..... 35

Figure 16: Example of voltage, current and SOC profiles according to speed profile over time

(in seconds) (Source: Pelletier et al. (2017)) ....................................................................... 39

Figure 17: Speed profile of WLTP test cycle (Source: SEAT UK (2019)) ............................. 40

Figure 18: Voltage change at different C-rate discharge (Source: Ho (2014)) ..................... 40

Figure 19: Charging curve of a typical lithium battery (Source: Cadex Electronics (2018)). . 41

Figure 20: Voltage change for discharge at different temperatures (Source: Ho (2014)) ..... 41

Figure 21: Capacity retention at different temperatures (Source: Ho (2014)). ...................... 42

Figure 22: Alternative stationary electrical energy storage technologies (Source: Thielmann et

al. (2015a)) ......................................................................................................................... 44

Figure 23: Aging (decrease of capacity) over number of cycles at different C-rates (Source:

Choi and Lim (2002)). ......................................................................................................... 47

Figure 24: Internal resistance over time at different temperatures (Source: Woodbank

Communications (2005)). .................................................................................................... 48

Figure 25: Efficiency degradation of cells under calendar ageing conditions (60°C, 100% SOC)

(Source: Redondo-Iglesias et al. (2018a)). .......................................................................... 48


10

Figure 26: Capacity loss as a function of charge and discharge bandwidth (Source: Xu et al.

(2018)). ............................................................................................................................... 49

Figure 27: Cycle life versus DOD and charging C-rate (Source: Pelletier et al. (2017)) ....... 49

Figure 28: Lifecycle characteristics of Panasonic CGR18650CG cylindrical cell (Source:

Panasonic (2008)) ............................................................................................................... 50

Figure 29: Number of full cycles before EOL is reached over DOD and depending on

temperature (Source: TractorByNet (2012)). ....................................................................... 50

Figure 30: Position of Nissan LEAF 40kWh battery (Source: Kane (2018)) ......................... 53

Figure 31: Kreisel Mavero home battery (Source: Kreisel Electric (2018)) ........................... 53

Figure 32: Mass flow diagram of batteries for EU28 in 2015 [tonnes] (Source: Stahl et al.

(2018)) ................................................................................................................................ 54

Figure 33: Estimated global second-use-battery energy [GWh] (source: Berylls (2018)). .... 55


11

List of tables

Table 1: Summary of data required for the calculation of EV base cases ............................ 33

Table 2: Summary of data required for the calculation of ESS base cases .......................... 36

Table 3: Standard test conditions for EV (Source: based on MAT4BAT Advanced materials for

batteries (2016), EnergyVille (2019) and Annex to Task 1) ................................................. 36

Table 4: Standard test conditions for ESS (Source: Annex to Task 1 and IEC 2015) .......... 38

Table 5: Real-life deviations from standard test conditions .................................................. 39

Table 6: Summary of data required for the calculation of EV base cases (indirect energy

consumption) ...................................................................................................................... 45

Table 7: Summary of data required for the calculation of ESS base cases (indirect energy

consumption) ...................................................................................................................... 46

Table 8: Comparison of service life of applications/base cases vs. maximum battery

performance (data was drawn from the preceding sections) ............................................... 51

Table 9: Assumptions referring to collections rates of EOL batteries (Source: Recharge

(2018)). ............................................................................................................................... 55

Table 10: Summary table of all relevant data (Sources according to the preceding section 58


12

3. Task 3: Users

The objective of Task 3 is to present an analysis of the actual utilization of batteries in different

applications and under varying boundary conditions as well as an analysis of the impact of

applications and boundary conditions on batteries’ environmental and resource-related

performance. The aims are:

to provide an analysis of direct environmental impacts of batteries during use phase

to provide an analysis of indirect environmental impacts of batteries during use phase

to provide insights on consumer behaviour regarding end-of-life-aspects

to identify barriers and opportunities of batteries linked to the local infrastructure

to make recommendations on a refined product scope and on barriers and

opportunities for Ecodesign

3.1. Subtask 3.1 - System aspects in the use phase affecting direct energy consumption

Subtask 3.1 aims at reporting on the direct impact of batteries on the environment and on

resources during the use phase. Direct impact refers to impact, which is directly related to the

function of the battery: the storage and provision of energy. Different scoping levels will be

covered in the analysis: first, a strict product approach will be pursued which is then broadened

to an extended product approach. After that, a technical system approach will follow, leading

to an analysis from a functional system perspective.

Strict product approach: In the strict product approach, only the battery system is

considered. It includes cells, modules, packs, a battery management system (BMS), a

protection circuit module (PCM) and passive cooling and heating elements (plates,

fins, ribs, pipes for coolants). The operating conditions are nominal as defined in

standards. Since relevant standards (e.g. IEC 62660, ISO 12405, IEC 61427-2, and

IEC 62933-2) already differentiate between specific applications, those will also be

discussed within this approach and base cases will be defined.

Extended product approach: In the extended product approach, the actual utilisation

and energy efficiency of a battery system under real-life conditions will be reviewed.

Further, the influence of real-life deviations from the testing standards will be

discussed. In that context, the defined base cases will be considered.

Technical system approach: Batteries, as defined in Task 1, are either part of a

vehicle or of a stationary (electrical) energy storage system, which comprise additional

components such as a power electronics (inverter, converter), chargers, active cooling

and heating systems and other application related equipment. However, energy

consumption of these components is considered indirect losses and thus, discussed

in chapter 3.2.

Functional approach: In the functional approach the basic function of battery

systems, the storage and provision of electrical energy, is maintained, yet other ways

to fulfil that function and thus other electrical energy storage technologies are

reviewed, as well.


13

3.1.1. Strict product approach to battery systems

As mentioned in Task 1, the product in scope is the battery system, referred to as battery. It

comprises one or more battery packs, which are made up of battery modules, consisting of

several battery cells, a battery management system, a protection circuit module, and passive

cooling or heating elements, such as plates, fins or ribs as well as coolant pipes (see Figure

1 within the red borderline). Active cooling and heating equipment, such as fans, heat

exchangers for tempering of coolant, heat pumps, heater elements etc., is usually located

outside of the battery system, thus cooling and heating energy is considered as indirect loss.

Furthermore, power electronics (e.g. inverter, converter), chargers and other application-

related equipment (see Figure 1) is located outside of the battery system as well and thus,

losses related to those components are also considered as indirect losses or even entirely out

of the scope of this study and thus external.

Depending on the application, the number of cells per module, of modules per pack and of

packs per battery or even the number of battery systems to be interconnected can vary.

Figure 1: Representation of the battery system components and their system boundaries.

Cell Cell Cell Cell

Module of cells

Cell Cell Cell Cell

Module of cells

Pack of modulesBattery Management System

Battery System

Temperature Sensor

Thermal Management

System

Protection Circuit Module

Cell Cell Cell Cell

Module of cells

Cell Cell Cell Cell

Module of cells

Pack of modulesBattery Management System

Battery System

Temperature Sensor

Thermal Management

System

Protection Circuit Module

Passive cooling/HeatingPo

wer

Ele

ctro

nic

s

Ap

plic

atio

n

Battery Application System

Indirect losses: Active Heating & Cooling / Chargers

Passive cooling/Heating


14

The primary function of a battery is to deliver and store electrical current at a desired voltage

range and accordingly the storage and provision of electrical energy. Consequently, following

the definition in Task 1, the functional unit (FU) of a battery is defined as one kWh of the total

energy delivered over the service life of a battery, measured in kWh at battery system level,

thus, excluding charger-, power electronics-, active cooling and heating equipment- as well as

application-related losses. This is in line with the harmonized Product Environmental Footprint

(PEF) for High Specific Energy Rechargeable Batteries for Mobile Applications (Recharge

2018). Accordingly, a battery is no typical energy-consuming product as for example, light

bulbs or refrigerators are, but it is an energy-storing and energy-providing product. Thus,

energy consumption that can directly be linked to a battery, as understood within that report,

is the battery’s efficiency in storing and delivering energy. Initially, the functional unit and the

battery efficiency will be defined, before standard testing conditions concerning battery

efficiency are reviewed and base cases are defined.

As already explained in Task 1, energy consumption during the use phase of a battery beyond

its efficiency can include losses from power electronics and losses during charge, discharge

and storage. Those will be modelled as ‘indirect system’ losses, which are part of a

subsequent section 3.2. This is a similar approach to the PEF where it is called delta approach

(EC 2018). It intends to model energy use impact of one product, in this case the battery, by

taking into account the indirect losses caused by another product, in this case the charger.

This means that the excess consumption of the charger shall be allocated to the product

responsible for the additional consumption, which is the battery. A similar approach is pursued

in section 3.2.

3.1.1.1. Key parameters for the calculation of the functional unit

The functional unit is a unit to measure the service that an energy related product provides for

a certain application. Key parameters of a battery that are related to the functional unit and

the links of those parameters to the Product Environmental Footprint pilot are the following:

Rated energy ERated (kWh) is the supplier’s specification of the total number of kWh

that can be withdrawn from a fully charged battery pack or system for a specified set

of test conditions such as discharge rate, temperature, discharge cut-off voltage, etc.

(similar to ISO 12405-4 “rated capacity”). E.g.: 80 kWh/full cycle

Capacity (Ah or kWh) is the total number of ampere-hours that can be withdrawn from

a fully charged battery under specified conditions (ISO 12405). Strictly, the ampere-

hours are used in the standards but this parameter can be also be expressed in

kilowatt-hours (see Task 1).

Depth of Discharge DOD (%) is the percentage of rated energy discharged from a

cell, module, and pack or system battery (similar to IEC 62281) (similar to PEF

“Average capacity per cycle”). Some tier 1 battery suppliers use DOD as the state of

charge window for cycling: e.g. 80%

Full cycle FC (#) refers to one sequence of fully charging and fully discharging a

rechargeable cell, module or pack (or reverse) (UN Manual of Tests and Criteria)

according to the specified DOD. It is similar to the PEF “Number of cycles”. The cycle

life of a battery (see section 3.1.1.2.1) is usually specified in FC. e.g. 1,500

Capacity degradation / State of Health SOHcap (%) refers to the decrease in capacity

over the lifetime (service life) as defined by a standard or declared by the manufacturer.


15

A SOHcap of 80% at the end of a battery’s service life (EOL) indicates a capacity

degradation of 20%. SOHcap is often indicated by SOH only. e.g. 80% SOHcap at EOL

The quantity of functional units of a battery QFU is the maximum number of kWh a

battery can deliver during its lifetime. It can be calculated as follows (the input figures

are just exemplary and could represent a battery-electric medium- to large-sized

vehicle):

𝑸𝑭𝑼 = 𝐸𝑅𝑎𝑡𝑒𝑑 ∗ DOD⏟ 𝑃𝐸𝐹 𝑒𝑛𝑒𝑟𝑔𝑦 𝑑𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑 𝑝𝑒𝑟 𝑐𝑦𝑐𝑙𝑒

∗ FC⏟𝑃𝐸𝐹 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑦𝑐𝑙𝑒𝑠

= 80kWh ∗ 80% ∗ 1,500FC

= 96,000 FU (kWh per battery lifetime)

Consequently, it is assumed, that the DOD defines the energy delivered per cycle and that the

absolute value of the energy delivered per cycle stays constant over the battery lifetime. This

can be justified by the BMS, that usually limits the usable battery capacity in such a way, that

the absolute DOD can be assured over the whole battery lifetime.

Figure 2: Visualisation of terms related to QFU calculation.

3.1.1.2. Standards for battery testing and testing conditions

Having a look at standards linked to the testing of battery cells and battery packs or systems,

numerous tests and testing conditions can be found in standards on batteries for electric

vehicles (EV) or for electrical energy storage systems (ESS).

General standard testing conditions for batteries can hardly be found. This is because (1) most

standards already focus on specific applications of battery cells and battery systems for

example in EV, such as battery-electric vehicles (BEV) or plug-in-hybrid-electric vehicles

(PHEV) (such as IEC 62660-1, ISO 12405-4) or in on-grid and off-grid ESS (IEC 61427-2 or

IEC 62933-2) and (2) for each test usually a big variety of testing conditions is specified.

Parameters that define the testing conditions in the IEC and ISO standards are:

C-rate nC (A), Current rate equal to n times the one-hour discharge capacity

expressed in ampere (e.g. 3C is equal to three times the 1h current discharge rate,

expressed in A) (ISO 12405-4).

Reference test current It (A): equals the rated capacity: Cn [Ah]/1 [h]. Currents should

be expressed as fractions of multiples or fractions of It. if n = 5, then the discharge

Percentage of ratedbattery energy

100%

80%

20%

0%EoL

time

SOHcapDOD

Cycle

availablenet capacity


16

current used to verify the rated capacity shall be 0.2 It [A] (IEC 61434).

Note: the difference between C-rate and It-rate is important for battery chemistries for

which the capacity is highly dependent on the current rate. For Li-ion batteries, it is of

minor importance. See for more information the section “Freedom in reference

capacity: C-rate and It-rate” in White paper (2018).

Temperature T / Room temperature RT (°C) which is a temperature of 25+/-2°C (ISO

12405-4)

State of charge SOC (%) is the available capacity in a battery pack or system

expressed as a percentage of rated capacity (ISO 12405-4).

with

Capacity C (Ah) as the total number of ampere-hours that can be withdrawn from a

fully charged battery under specified conditions (ISO 12405-4)

Rated capacity Cn (Ah) which is the supplier's specification of the total number of

ampere-hours that can be withdrawn from a fully charged battery pack or system for a

specified set of test conditions such as discharge rate, temperature, discharge cut-off

voltage, etc. (ISO 12405-4). The subscript n refers to the time base (hours) for which

the rated capacity is declared (IEC 61434). In many standards, this is 3 or 5.

3.1.1.2.1. Key parameters for the calculation of direct energy consumption of batteries in

applications (application service energy)

In the context of this study, it is not useful to go into the details of all of the above-mentioned

standards, tests and test conditions, but to select the most important ones who are related to

energy consumption. In order to be able to determine the direct energy consumption of a

battery based on the quantity of functional units of a battery system, the following parameters,

mainly referring to IEC 62660 and ISO 12405-4, are to be considered. IEC 62660 relates to

the cell level, whereas ISO 12405 relates to the battery system level. For this study, according

to the definition of the strict product approach, the system level has to be taken into

consideration:

Energy efficiency ηE (energy round trip efficiency) (%) - each FU provided over the

service life of a battery is subject to the battery’s energy efficiency. It can be defined

as the ratio of the net DC energy (Wh discharge) delivered by a battery during a

discharge test to the total DC energy (Wh charge) required to restore the initial SOC

by a standard charge (ISO 12405-4). E.g. 96% (PEF)

o In most standards, energy efficiency of batteries is measured in steady state

conditions. These conditions usually specify temperature (e.g. 0°C, RT, 40°C,

45°C), constant C-rates for charge and discharge (discharge BEV 1/3C, PHEV

1C according to IEC 62660, charge by the method recommended by the

manufacturer) as well as SOCs (100%, 70%; for BEV also 80% according to

IEC 62660, 65%, 50%, and 35% for PHEV according to 12405-4)

o For batteries used in PHEV however, in ISO 12405-4 for example, energy

efficiency is also measured at a specified current profile pulse sequence, which

is closer to the actual utilisation, including C-rates of up to 20C.

o For batteries used in ESS, in IEC 61427-2 for example, also load profiles for

testing energy efficiency are defined (see Figure 3)


17

Self-discharge/charge retention SD (%SOC/month) - each battery that is not under

load loses part of its capacity over time (temporarily). Charge retention is the ability of

a cell to retain capacity on open circuit under specified conditions of storage. It is the

ratio of the capacity of the cell/battery system after storage to the capacity before

storage (IEC 62620). E.g. 2%/month

o Self-discharge of EV batteries is measured by storing them at 45°C, 50% SOC

and for a period of 28 days (IEC 62660-1), or at RT to 40°C and 100% SOC for

BEV, 80% SOC PHEV with a fully operational BMS (ISO 12405-4), storing the

batteries for 30 days

o The remaining capacity after the self-discharge period is measured at 1C for

PHEV and 1/3C discharge for BEV, leading to the self-discharge.

Cycle life LCyc (FC) is the total number of full cycles a battery cell, module or pack can

perform until it reaches its End-of-Life (EOL) condition related to its capacity fade or

power loss (EOL will be further explained in section 3.3). E.g. 1,500 FC

o Cycle life of EV batteries is determined by using specified load profiles for

PHEV and BEV application (see Figure 4) at temperatures between RT and

45°C

o PHEV cycle life tests cover SOC ranges of 30-80% and C-rates of up to 20C.

If the manufacturer's specified maximum current is lower than 20C, then the

test profile is adapted in a predefined way.

o BEV cycle life tests cover SOC ranges of 20-100%

Calendar life LCal/storage life (a) is the time in years, that a battery cell, module or

pack can be stored under specified conditions (temperature) until it reaches its EOL

condition (see also SOH in section 3.1.1.2.3). It relates to storage life according to IEC

62660-1, which is intended to determine the degradation characteristics of a battery.

E.g. 15 years

o Ambient conditions for the determination of calendar life are 45°C and a

measuring period of three times 42 days

o Initial SOC for (P)HEV is at 50%, the discharge after storage takes place at 1C

o Initial SOC for BEV is at 100%, the discharge after storage takes place at 1/3C

The actual service life of a battery cell, module, pack or system is defined by the minimum of

cycle life and calendar life.

Figure 3: Typical ESS charging/discharging cycle (IEC 62933-2)


18

Figure 4: Cycle test profile PHEV (left) and BEV (right) (discharge-rich) (ISO 12405-4)

The application service energy (AS) (kWh) is the total energy required by the application

over its lifetime in kWh. With the lifetime of an application (13 years), the number of annual

full cycles FCa (FC/a) (e.g. 60 FC/a), a rated energy of 80 kWh and 80% DOD it can be

calculated as follows:

𝐴𝑆 = 𝐿𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛 ∗ 𝐹𝐶𝑎 ∗ 𝐸𝑟𝑎𝑡𝑒𝑑 ∗ 𝐷𝑂𝐷

= 13 ∗ 60 ∗ 80 ∗ 80% = 49,920 𝑘𝑊ℎ

This formula can be used for all types of applications, when the number of annual full cycles

is given. For EVs, given that data on annual all-electric vehicle kilometres travelled (VKT) (e.g.

14,000 km), energy consumption of the vehicle (0, 20 kWh/km) and recovery braking (20%) is

available, the following formula can be used:

𝐴𝑆𝐸𝑉 = 𝐿𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛 ∗ 𝑎𝑛𝑛𝑢𝑎𝑙 𝑎𝑙𝑙-𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝑉𝐾𝑇 ∗ 𝑒𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 ∗ (1

+ 𝑟𝑒𝑐𝑜𝑣𝑒𝑟𝑦 𝑏𝑟𝑎𝑘𝑖𝑛𝑔

= 13 ∗ 14,000 ∗ 0,20 ∗ (1 + 20%) = 43,680 𝑘𝑊ℎ

If the AS is higher than the QFU of the battery used in that specific application, more than one

battery is required for that application, and thus, a battery replacement is required. The

following formula is applied for the calculation of the number of batteries needed to fulfil the

application service:

𝑁𝑏𝑎𝑡 =𝐴𝑆

𝑄𝐹𝑈=43,680

96,000= 0.46

Since that figure is lower than one, in that example, there is no need for a battery replacement.

The actual lifetime (service life) of a battery, as a simplification, is determined by the minimum

of cycle life and calendar life (in reality, a superposition of both aging effects takes place).

Whichever is reached first, determines the end of life. Thus, it can be calculated as follows:

𝑠𝑒𝑟𝑣𝑖𝑐𝑒 𝑙𝑖𝑓𝑒𝑏𝑎𝑡 = min{LCyc; LCalFCa}


19

As explained above, when using batteries losses occur due to battery energy efficiency and

self-discharge. With an average state of charge SOCAvg (%) of 50%, the losses can be

calculated as follows:

𝐋𝐨𝐬𝐬𝐞𝐬 = 𝑄FU ∗ (1 − ηE) + SD ∗ min {LCyc

FCa; LCal} ∗ 12

⏟ actual service life in months

∗ SOCAvgERated

= 86,400 ∗ (1 − 0,96) + 0,02 ∗ min {1,500

60; 15} ∗ 12 ∗ 50% ∗ 80

= 3,840 + 192 = 4,032

For the exemplary figures chosen, the impact of a battery’s energy efficiency on its direct

energy consumption is a lot higher than the effect of self-discharge. Further, ERated, DOD, cycle

life as well as calendar life, but also the actual annual utilisation of the battery shows high

impact on the AS and thus, on the direct energy consumption of a battery.

3.1.1.2.2. Key parameters for the calculation of battery energy efficiency

As we could show in chapter 3.1.1.2.1, the energy efficiency of a battery has strong impact on

its direct energy consumption. Consequently, the battery energy efficiency will be reviewed

more detailed. The key parameters of a battery that are required for calculating its efficiency

are the following:

Voltaic efficiency ηV (%) can be defined as ratio of the average discharge voltage to

the average charge voltage. The charging voltage is always a little higher than the

rated voltage in order to drive the reverse chemical (charging) reaction in the battery

(Cadex Electronics 2018).

Coulombic efficiency ηC (%) is the efficiency of the battery, based on charge (in

coulomb) for a specified charge/discharge procedure, expressed by output charge

divided by input charge (ISO 11955).

With V, I and T as average Voltage, average Current and Time for C Charge and D

Discharge the battery energy efficiency can be calculated as follows (Recharge

2018):

𝑬𝒏𝒆𝒓𝒈𝒚 𝒆𝒇𝒇𝒊𝒄𝒊𝒆𝒏𝒄𝒚 = (𝑉𝐷𝑉𝐶)(𝐼𝐷 ∗ 𝑇𝐷𝐼𝐶 ∗ 𝑇𝐶

) = (𝑣𝑜𝑙𝑡𝑎𝑖𝑐 𝑒𝑓𝑓𝑖𝑒𝑛𝑐𝑦)(𝑐𝑜𝑢𝑙𝑜𝑚𝑏𝑖𝑐 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦)

Li-ion batteries have a coulombic efficiency close to 100% (better than 99.9% according to

Gyenes et al. (2015)) (no side reaction when charged up to 100%). Consequently, the voltaic

efficiency is the main lever concerning the battery energy efficiency. It is always below one

because of the internal resistance of a battery, which has to be overcome during the charging

process, leading constantly to higher charging voltages compared to discharging voltages.

Consequently, a higher discharge voltage as well as a lower charge voltage, while all other

parameters are kept unchanged, improve efficiency. Figure 5 shows charge and discharge

voltages for two different cell chemistries (nickel manganese cobalt (NMC) and lithium iron

phosphate (LFP)) in relation to the SOC and the resulting efficiency. It has to be mentioned

however, that the scope of this study is not limited to those cell chemistries (see also Task 1

and Task 4). First it can be seen, that charge voltage is higher than discharge voltage for both

cell chemistries. Second, the efficiency of NMC cells in monotonically increasing with SOC.

Third, the efficiency of LFP decreases rapidly in the extremities (0 and 100% SOC) (Redondo-

Iglesias et al. 2018a).


20

Figure 5: Charge and discharge voltages (left y-axis) and efficiency (right y-axis) of fresh cells

(Source: Redondo-Iglesias et al. (2018a)).

Different cell chemistries and designs can be differentiated (see Task 4), which also differ in

energy efficiency. According to Redondo-Iglesias et al. (2018a) for Lithium Iron Phosphate

batteries an energy efficiency of around 95% can be assumed, while for Lithium Nickel

Manganese Cobalt Oxide an energy efficiency of 96% at cell level is assumed. Recharge

(2018) also assume 96% energy efficiency as an average. Including losses due to the BMS

(thermal managements system, protection circuit module) leads, according to Schimpe et al.

(2018) and expert interviews to a battery efficiency on system level, as defined within this

study, of 92%.

Furthermore, it has to be noted that the energy efficiency strongly depends on the

charge/discharge currents (C-rate, power) for given cell chemistry and design (see formula

above).

However, it has to be mentioned that these statements are not generalizable. Battery cell

characteristics depend on much more than the cathode material only. Any other component

(e.g. anode, electrolyte, separator), size and format (cylindrical, pouch, prismatic; see Task 4)

as well as the combination of materials and the manufacturing process largely influence the

cell characteristics. Consequently, generalizable statements when comparing for example

NMC and LFP cells, regarding cycle life or safety, can hardly be made and have to be treated

with caution.


21

3.1.1.2.3. Further parameters related to battery efficiency and affected energy

Besides the parameters that have already been described and discussed, further terms and

definitions referring to batteries, battery efficiency and affected energy have to be introduced:

Energy E (kWh) is the total number of kWh that can be withdrawn from a fully charged

battery under specified conditions (similar to ISO 12405-1 “capacity”).

State of health SOH (%) defines the health condition of a battery; however, no

definition can be derived from standards. It can be described as a function of capacity

degradation, also called capacity fade (see ISO 12405-4) and internal resistance.

Depending on the application, a battery can only be operated until reaching a defined

SOH, thus, it relates to the service life of a battery.

Internal resistance R (Ω) is the resistance within the battery, module, pack or system.

It is generally different for charging and discharging and dependent on the current, the

battery state of charge and state of health. As internal resistance increases, the voltaic

efficiency decreases, and thermal stability is reduced as more of the

charging/discharging energy is converted into heat.

Rated voltage VR (or nominal Voltage) (V) is a suitable approximate value (mean

value between 0% and 100% DOD) of the voltage during discharge at a specified

current density used to designate or identify the voltage of a cell or a battery (IEC

62620).

Voltage limits VL (V) define the maximum and minimum cut-off voltage limits for safe

operation of a battery cell. The maximum voltage is defined by the battery chemistry.

For Lithium-ion battery (LIB) cells of LCO, NCA and NMC type 4.2 V are typical

voltages. For LFP type, it is 3.65 V. However, the voltages mentioned are operational

limits that should be kept in order to reach a certain battery cycle life. There are also

higher voltage limits that relate to safety aspects. The battery is fully charged when the

difference between battery voltage and open circuit voltage is within a certain range.

Open circuit voltage VOC (V) is the voltage across the terminals of a cell or battery

when no external current is flowing. (UN Manual of Tests and Criteria).

Volumetric energy density (Wh/l) is the amount of stored energy related to the

battery pack or system volume and expressed in Wh/l (ISO 12405-4).

Gravimetric energy density (Wh/kg) is the amount of stored energy related to the

battery pack or system mass and expressed in Wh/kg (ISO 12405-4).

Volumetric power density (W/l) is the amount of retrievable constant power over a

specified time relative to the battery cell, module, and pack or system volume and

expressed in W/l.

Gravimetric power density (W/kg) is the amount of retrievable constant power over

a specified time relative to the battery cell, module, pack or system mass and

expressed in W/kg.


22

Figure 6 shows a typical data sheet of a battery system for use in heavy-duty vehicles. Most

of the parameters and terms that have been introduced within that study can be found on that

data sheet. The calendar life and the energy efficiency of the battery system, however, is not

stated in the data sheet.

Figure 6: Typical battery system data sheet (Source: Akasol (2018)).

3.1.1.3. Definition of base cases

Looking at the global battery demand (see Task 2), EV and stationary ESS stand out,

especially referring to future market and growth potential. Besides the BEV and PHEV

markets, large scale ESS also show high growth rates in future. A bit lower, but still substantial

are growth rates for residential ESS according to Task 2 report. In EV applications, the main

purpose of batteries is supplying electrical energy to electric motors that are providing traction

for a vehicle. In stationary applications they balance load (supply and demand for electricity)

and consequently store electrical energy received from the grid or directly from residential

power plants (such as photovoltaic (PV) systems or block-type thermal power stations) or

commercial power plants (renewable or non-renewable energy sources) and feed it back to

the grid or energy consumers.

Since for the two mentioned fields of application, EV and ESS, numerous specific applications

can be distinguished, they have to be narrowed down further. As the purpose of this report is

to identify the impact of batteries on energy consumption, those applications should be

selected for further analyses that have the highest energy consumption. For the EV field


23

greenhouse gases (GHG) from road transport are regarded as a useful proxy for energy

consumption. Figure 7 shows, that the highest share of GHG can be attributed to passenger

cars with more than 60%. These are followed by heavy-duty trucks and buses. Light duty

trucks, motorcycles and other road transportation play a minor role only and are therefore not

considered further.

Figure 7: GHG emissions from road transport in the EU28 in 2016 by transport mean [%]

(Source: European Commission (2018)).

In 2017 more than 15 mio. new passenger cars were registered in the EU28 (European

Commission 2018) in contrast to less than 2 mio. light commercial vehicles/light duty trucks,

which stresses the importance of passenger cars. Light commercial vehicles and light duty

trucks weigh less than 3.5 tonnes and thus, are more similar to passenger cars, than to

medium- or heavy-duty trucks. Due to the similarity of light commercial vehicles and passenger

cars in terms of battery capacity, fuel consumption or annual mileage, light commercial

vehicles are not considered as an own base case but considered to be represented by the

passenger car base cases.1 Passenger cars have, in terms of registrations but also in terms

of GHG emissions, by far the highest share in road transport. For that reason and since many

different passenger car segments exist, which should be represented in that study, two

passenger car types are considered: small-sized cars and medium- to large-sized cars.

Furthermore, 370,000 medium-and heavy-duty trucks were registered in the EU28 in 2017,

while only 42,000 buses and coaches were registered (European Commission 2018). Beyond

that, the technical characteristics of buses, such as battery capacity, fuel consumption or

annual mileage, do not differ significantly from the characteristics of HDT.2 For that reason,

1 Battery capacities of light commercial vehicles, which are already on the market, range between 20

kWh (Iveco Daily Electric, Nissan e-NV200 Pro, Streetscooter Work Box, Citroen Berlingo Electrique)

and 40 kWh (EMOVUM E-Ducato, Mercedes-Benz eSprinter and eVito (Schwartz 2018). Furthermore,

for the light commercial vehicle Renault Kangoo Z.E. Boblenz (2018) states a fuel consumption of 15,2

kWh/100km according to the NEDC which is converted to 19kWh/100km according to the EPA FTP.

Finally, light commercial vehicles are driven 15,500 km on average per year in the UK (Dun et al. 2015)

and 19,000 km in Germany (KBA 2018), which is just slightly higher than for passenger cars.

2 The battery capacity of urban buses ranges between 80 kWh and 550 kWh (Electrek 2017; VDL Bus

& Coach 2019), while most of the buses have a battery capacity of around 200 kWh. Aber (2016) states

an average energy consumption of 125 kWh per 100 km and according to Papadimitriou et al. (2013)

urban buses travel on average between 40.000 and 50.000 km a year, while coaches travel up to

60.000 km on average.


24

considering buses as an own base case would not lead to significant new insights, regarding

the Ecodesign process.

Trucks can be further differentiated according to their gross vehicle weight (GVW) in medium-

duty trucks (up to 16 tonnes GVW) and heavy-duty trucks (HDT) (more than 16 tonnes GVW).

Since the registrations of HDT are three times higher than those of medium-duty trucks

(European Commission 2018), the former will be in the focus of this study. HDT can be heavy-

duty straight trucks, semi-trailer trucks, or tractor units, referred to as heavy-duty tractor units

(HDTU).

Regarding passenger cars, BEV and PHEV are the most promising battery-related

applications (Gnann 2015). For HDT also battery-electric vehicles seem to be very

promising, while for HDTU plug-in-hybrid solutions seem to be promising (Wietschel et al.

(2017)).

There are currently four potential main applications for stationary ESS (see also Task 2): PV

battery systems, peak shaving, direct marketing of renewable energies and the provision of

operating reserve for grid stabilization in combination with multi-purpose design (Michaelis

2018). Since PV battery systems, referred to as residential ESS and the provision of

operating reserve and multi-purpose design, referred to as commercial ESS, seem to have

the highest market potential (see Thielmann et al. (2015b) and Task 2), they will be in the

scope of this study.

The most promising battery technology (see Task 4) for both fields of application, EVs as well

as ESS are large-format lithium-ion batteries. This is due to their technical (in particular energy

density, lifetime) as well as economic (cost reduction) potential. It has to be noted that the

product scope is still the battery system as defined in section 3.1.1. However, the utilization of

the battery, represented by a load profile for example, as well as battery capacity varies.

To sum it up the following applications are in the scope of this study and define base cases:

EV applications:

passenger BEV (medium to large)

passenger BEV (small)

passenger PHEV

battery-electric HDT

plug-in-hybrid HDTU

Stationary applications:

residential ESS

commercial ESS

The base cases defined above have certain requirements concerning technical performance

parameters, such as energy densities, calendar and cycle life, C-rates (fast loading

capabilities) and tolerated temperatures, which will be defined in the following sections.


25

Parameters for the definition of base cases

Looking at the formula for the calculation of the direct energy consumption of batteries (see chapter 3.1.1.2.1), the following parameters have to be defined for all base cases:

Rated battery capacity

Depth of discharge

Annual full/operating cycles base case

Calendar life base case

Energy efficiency battery

3.1.1.3.1. Base cases for EV applications

Rated battery capacity on application level

The required and suitable rated battery capacity highly depends on the actual vehicle type.

The bigger and heavier a car is, the larger the battery capacity should be. Currently for BEV

20 to 100 kWh (Tesla Model S and X) are common battery capacities, although larger battery

capacities might be available for special sport cars. PHEV usually have a battery capacity of

4 to 20 kWh. Medium- to large-sized cars currently have a battery capacity between 60 and

100 kWh. Therefore, we take 80 kWh for the base case BEV (medium to large). The current

sales-weighted average of rated battery capacity for passenger BEV in Europe is 39 kWh,

thus we assume 40 kWh to be the battery capacity of small-sized passenger BEV. For

passenger PHEV the average is at 12 kWh and stayed almost constant (see Figure 8).

Therefore, we assume 12 kWh for PHEV.

Figure 8: Sales-weighted average of xEV battery capacities for passenger cars [kWh] (Source:

ICCT (2018))

In contrast to passenger cars, no battery-electric HDT (between 12 and 26 to gross vehicle

weight (GVW)) is available on the market. So far, only some pre-series trucks are tested by

selected customers (Daimler 2018; MAN Truck & Bus AG 2018). Nevertheless, truck OEM

specified technical details for their announcements, ranging from 170 kWh battery energy of

a DAF CF Battery Electric up to a Tesla Semi (HDTU) with 1,000 kWh battery capacity (Honsel


26

2018). Most of the battery capacities currently stated range between 200 and 300 kWh,

however, a further increase can be expected for the future and thus, 360 kWh is assumed for

the base case. According to Hülsmann et al. (2014) and Wietschel et al. (2017) for long range

HDTU purely battery-electric trucks seem not to be a proper solution. They argue that range

and costs of battery-electric HDTU are not competitive. As mentioned, some truck

manufacturers however, such as Tesla (Tesla Semi) and Daimler (Freightliner eCascadia)

announced HDTUs with ranges of 400 to 800 km being provided by a huge battery.

Nevertheless, two drawbacks are linked with high battery capacities: First, because of their

high weight, they significantly reduce payload, which is hardly acceptable for truck operators.

Second, big batteries, besides their negative ecological impact, which is increasingly

discussed in public and the limited availability of resources, are very expensive. Since in a

business context (e.g. logistics service providers), economic aspects and as such especially

the total cost of ownership of operating a truck are decisive, from the current point of view

battery-electric trucks don't have a high market potential, thus plug-in hybrid HDTU are

considered. Following Hülsmann et al. (2014) and Wietschel et al. (2017), a battery energy of

160 kWh is assumed for PHEV HDTU.

Depth of DischargeReferring to Hülsmann et al. (2014) for BEV applications a DOD of 80%

is assumed. For PHEV applications, 75% DOD seems to be reasonable, according to expert

interviews.

Annual full/operating cycles and calendar life base case

The number of operating cycles3 per year can be retrieved by dividing the all-electric annual

vehicle mileage by the all-electric range of the vehicles. Thus, first the all-electric annual

mileage of vehicles has to be determined, before the all-electric range and the calendar life of

the base cases are defined.

Annual mileage

Although it is argued, that driving profiles of ICEV (internal combustion engine vehicles) and

BEV or PHEV might differ (Plötz et al. 2017a) (on the one hand the range of EV is limited but

on the other hand their variable costs are comparably low in contrast to their high fixed costs,

resulting in high annual mileages being beneficial for EV) for this study it is assumed, that the

same annual mileage and driving patterns apply to all powertrains. Further, for simplification

reasons we do not thoroughly review distinct (daily) driving patterns and profiles but average

annual and daily driving distances. However, taking Figure 9 into consideration it becomes

clear that average values are just a rough approximation of the actual daily driving distances,

which can vary greatly in size.

3 For EV operating cycles are calculated, since data can be retrieved more easily than for the calculation

of full cycles.


27

Figure 9: Daily and single route driving distances of passenger cars in Germany (Source:

Funke (2018)).

The average vehicle kilometres travelled (VKT) per passenger car and year in the EU28 is

approximately 14,000 km for medium-large passenger cars and 11,000 km for small

passenger cars according to Papadimitriou et al. (2013). Further, the average retirement age

of medium-large cars is around 13 years, while for small cars it is 14 years. However, the

service life of EVs could be longer than that of ICEV because of less mechanical parts

subjected to failure risk.

HDT drive on average 50,000 km per year in the EU28 (Papadimitriou et al. 2013). Further,

the average for HDTU is 100,000 km per year. The typical operating life is 14 years for HDT

and 12 years for HDTU in the EU(Papadimitriou et al. 2013).

All-electric range and mileage

For BEV, naturally the entire annual mileage is driven all electric. Plötz et al. (2017b) find, that

in Germany each passenger car is used on 336 of 365 days of the year, thus 40 km is the

assumed daily all-electric mileage of a BEV passenger car. Further, the all-electric driven

share of passenger PHEV is calculated by Plötz et al. (2017a) and it is about 40-50% with 40

km all-electric range. Since the base case PHEV’s all electric range is 50 km (battery capacity

multiplied with DOD, divided by energy consumption; required values to be discussed in the

next paragraphs) 50% all electric mileage is assumed, leading on an annual basis to 7,000

km. HDT drive on 260 days per year (daily ~190 km all-electric for HDT and 380 km for HDTU)

(Wietschel et al. 2017). Since the all-electric range of HDT is 240 km (same calculation as for

passenger PHEV) no intermediate charging is required. HDTU have an all-electric range of

only 86 km, thus intermediate charging is required for achieving high all-electric VKT. The

HDTU however is continuously on the road, only making stops in order to account for

mandatory periods of rest. A break of 45 to 60 minutes for fast charging should be sufficient,

in order to fully recharge the battery, leading to a daily range all-electric range of 140 km,

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28

which might be increased further by mandatory breaks. Thus, we conclude, that 50,000 of the

100,000 kilometres per year might be driven all-electric by the HDTU.

The all-electric ranges of EVs can either be derived from measurements based on official test

cycles or calculated by multiplying the rated energy by the DOD and dividing the result by the

energy consumption of the vehicle (the latter approach is less accurate and it is therefore

neglected). The energy consumption in that case also has to be derived from measurements

according to official driving cycles.

EV energy consumption

The application service energy of a vehicle can roughly be differentiated in energy required

for traction and energy required by ancillary consumers, such as entertainment systems, air

conditioning or light machine, servo steering and ABS. Figure 10 shows the energy

consumption [kWh] and distribution of a Nissan Leaf (2012) on a specific drive cycle (~12km).

Around 30% of the energy provided to the electric motor can be fed back into the battery due

to regenerative braking (explained below). However, for the base cases we assume 20% as

a conservative assumption. The accessories load sums up to approximately 3%. However, it

is important to note, that referring to these figures no cooling or heating of the driver cabin is

included. This can increase energy consumption by around 25%.

Figure 10: Battery energy efficiency losses of Nissan Leaf (2012) (Source: Lohse-Busch et al.

(2012))

All of the energy consumed within a BEV (leaving out auxiliary lead-acid batteries), the total

energy consumption of the vehicle has to be delivered by the battery, which is also true for the

electric mode of PHEV.

The energy required by a vehicle for its traction can be calculated as follows (Funke 2018):

∫1

𝜂𝑃𝑇(

1

2𝑐𝑑𝜌𝐴𝑣

2

⏟ 𝑎𝑒𝑟𝑜𝑑𝑦𝑛𝑎𝑚𝑖𝑐 𝑑𝑟𝑎𝑔 𝑟𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑐𝑒

+ 𝑐𝑟𝑚𝑔⏟ 𝑟𝑜𝑙𝑙𝑖𝑛𝑔 𝑟𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑓𝑜𝑟𝑐𝑒

+ 𝑚𝑎⏟𝑚𝑎𝑠𝑠 𝑎𝑐𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛

) ∗ 𝑣 𝑑𝑡

With ηPT being the efficiency of the vehicle’s powertrain (electric motor, gearbox, power

electronics), cd as drag coefficient, ρ as density of fluid [kg/m3] (1.2 kg/m3 for air), A as


29

characteristic frontal area of the body [m2], v as flow velocity [m/s] (driving speed), cr as rolling

resistance coefficient, m as mass of body [kg], g as acceleration of gravity [m/s2] and a as

lengthways acceleration of the vehicle. When considering the traction energy requirements of

a vehicle, one can see that it substantially depends on the vehicle’s speed (to the power of

three) but also on the vehicle’s mass. This is where the impact of the battery weight on

energy consumption becomes clear. Furthermore, payload plays an important role, especially

for commercial vehicles. Since for example the battery weight of a Tesla Model S can be as

high as 500 kg, an impact of battery weight on the traction energy consumption and

consequently on the total fuel consumption can be expected. Detailed calculations cannot be

part of that study, but as a rough estimation for each additional 25 kWh battery energy an

increase in fuel consumption of 1 to 2 kWh/100km can be expected, while in future due to

improvements of gravimetric energy density 0.5 to 1 kWh/100 km might be possible (Funke

2018).

What can also be seen from the formula presented is that vehicle speed and acceleration and

consequently individual driving behaviour have a strong impact on fuel consumption.

Energy consumption measured with standard tests

For the assessment of passenger cars’ emissions and fuel economy the Worldwide

Harmonized Light Vehicle Test Procedure (WLTP) just recently replaced the New

European Driving Cycle (NEDC) as reference drive cycle. It was established in order to

better account for real-life emissions and fuel economy and it uses a new driving/speed profile

(see Figure 11). The WLTP comprises 30 instead of 20 minutes of driving; it includes more

than twice the distance and less downtime compared to the NEDC. Further, the average speed

is 46.5 km/h instead of 34 km/h; also, a cold engine start is carried out, while air conditioning

use is still not considered. Plötz et al. (2017a) argue, that fuel consumption of cars measured

with the WLTP is closer to real-life fuel consumption, but it is still not accurate.

Figure 11: Comparison of speed profiles for WLTP and NEDC (Source: VDA (2018))

They consider the use of the Federal Test Procedure (FTP) of the U.S.-American

Environmental Protection Agency (EPA) more accurate and very close to real-life behaviour

(see speed profile in Figure 12). This is mainly because the FTP includes AC use and hot and

cold ambient temperatures, both having big impact on the fuel consumption. That is why for

the fuel consumption of the reference applications, if available, values measured with the FTP

are used. According to Plötz et al. (2017a) the all-electric driving range, and thus also fuel

Speed in km/h


30

consumption of vehicles measured with the NEDC can be assumed to be 25% lower than

when measured with the FTP.

Figure 12: Speed profile of EPA Federal Test Procedure (Source: EPA (2018)).

Fueleconomy.gov (2018) provides a database of energy consumption of passenger BEV and

PHEV. Analyzing the fuel consumption of medium-large and small BEV as well as PHEV and

including efficiency gains in the near future, we assume that the base case BEV (medium and

large) will consume 20 kWh/100km, while BEV (small) will consume 16 kWh/100km and PHEV

around 18 kWh/100 km. No fuel consumption is specified for HDT, but from range

specifications of the Daimler eActros a fuel consumption of 120 kWh/100km can be derived.

Comparing that figure to Hülsmann et al. (2014), Hacker et al. (2014) and Wietschel et al.

(2017) it can be confirmed . For a HDTU a fuel consumption of 140 kWh/100km can be derived

from Wietschel et al. (2017) .

A big advantage of BEV and PHEV, that helps increasing the range, is the potential brake

energy recovery (regenerative braking, or braking energy recuperation). During braking, a

certain share of the kinetic energy can be recovered when using the electric motor as a

generator, feeding back energy to the battery.

Gao et al. (2018) state that about 15% of battery energy consumption could be recovered with

a 16t battery-electric delivery truck, while Xu et al. (2017) find, that 11.5% of the battery

energy consumption could be recovered - 12% is used as a conservative assumption.

Furthermore Gao et al. (2015) find, that a plug-in electric HDTU (parallel-hybrid with diesel

engine) is able to reduce total fuel consumption by 6 to 8% although there is not much kinetic

energy recovery. The reason is associated with the more optimal utilization of the engine map.

It is assumed, that the fuel consumption is reduced by 6% on average through energy

recovery, no matter if it is a plug-in-hybrid truck with a diesel engine, fuel cell or catenary

system.


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Figure 13: Current change curves (Source: Xu et al. (2017))

Calendar and cycle life of battery

It is desirable that the battery’s cycle and calendar life coincides with the vehicle lifetime.

Nevertheless, especially for high annual vehicle mileage, this might not be feasible, since for

batteries that are used in BEV a cycle life of 1,500 full cycles and for batteries used in PHEV

a cycle life 2,000 full cycles are assumed (according to experts), before the batteries reach

EOL condition (assuming no calendar aging). Batteries for HDT and HDTU have to be

designed for higher annual mileage, thus, 2,000 full cycles are assumed for BEV HDT and

3,000 full cycles for PHEV HDTU (based on expert interviews). Further, a maximum calendar

life of the installed battery (assuming no cycling) of 20 years seems reasonable for all EV

applications according to experts (high power or high energy required). Those lifetime figures

might require full or partial battery changes concerning the applications (see chapter 3.3.2).

An important aspect that would have impact on the battery's lifetime is the potential provision

of demand side flexibility by BEVs and PHEVs. One option is controlled or smart charging of

EVs regarding flexible timing and charging power, which is tested and partially already

implemented (controlled/delayed/smart grid-to-vehicle G2V). Smart charging can be operated

by smart charging devices or by the distribution grid operator. Smart charging devices can

optimize the charging of EVs economically from a user perspective by profiting from flexible

electricity tariffs. A positive side-effect can be a reduced capacity degradation of the EV due

to on average reduced SOCs (González-Garrido et al. 2019). Grid operator controlled smart

charging reveals load-shifting potential to the distribution grid operator. Having load-shifting

potential at hand reduces required grid expansion, which is due to the additional load caused

by EVs, but it might also lead to "un-optimal" charging, which could decrease battery lifetime.

Another option is, that EVs, which are idle and connected to the grid could be used as flexible

energy storage, feeding energy back into the grid (vehicle-to-grid V2G) for which EV owners

would get a compensation. This would cause additional cycling and thus reduce the battery

lifetime. EVTC (2017) was able to show that delayed G2V charging does not have negative

impact on the battery, while González-Garrido et al. (2019) even showed a positive effect on

the battery. Both studies agree, however, that V2G charging accelerates capacity degradation

significantly. González-Garrido et al. (2019) state an increased degradation of 15 to 30%

depending on V2G power, while Jafari et al. (2018) state an additional battery degradation of


32

14 to 37% depending on the type of service provided (frequency regulation, peak shaving or

solar energy integration).

Energy efficiency

As already explained above, the energy efficiency of a battery depends on the operating

conditions. Assuming optimum temperatures, provided by a TMS, C-Rate is the deciding

factor. For BEV at an average C-rate for charging and discharging of 0.5C the energy

efficiency of the battery is about 96%. This figure relates to DOE (2012) where it is stated, that

at the most demanding drive cycle an average battery efficiency of 95% can be measured.

Since the most demanding drive cycle is not the most representative drive cycle we assume

a slightly higher efficiency of 96%. For PHEV the same energy efficiency is assumed.

As explained in section 3.1.1.2.1, the application service energy for EVs can be calculated by

either using detailed data on actual vehicle and driving characteristics or by using an assumed

number of full cycles. Taking all data and assumptions into account, an annual number of full

cycles and thus charging of 120 can be estimated for all passenger cars it seems reasonable

that they are charged on every third day. Because of the much more frequent use, for the HDT

base case 300 full cycles and for the HDTU 600 full cycles can be assumed. Beyond that,

many figures that have been discussed, such as battery energy efficiency, self-discharge,

battery calendar or cycle life, energy consumption of the vehicle etc. were assumed to be

static as they are defined in several battery, vehicle and ESS testing standards. It has to be

mentioned, however, that those figures highly depend on the actual utilization of batteries,

which change according to temperature and actual driving/load profiles of the applications, for

example. In this section, those deviations are not taken into consideration.

The data discussed in the previous paragraphs is summed up in Table 1. We included

application specific parameters such as lifetime, VKT, energy consumption, range, DOD and

typical range of the battery capacity in that application. Further, we calculated the quantity of

functional units according to section 3.1.1.1 and the application service energy as well as

energy consumption due to battery energy efficiency and due to self-discharge according to

section 3.1.1.2.1 for each application. Those figures are related to the strict product approach.


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Table 1: Summary of data required for the calculation of EV base cases

Passenger

BEV

(medium to

large)

Passenger

BEV (small)

Passenger

PHEV HDT BEV HDTU PHEV

Economic lifetime of the

application [a]

13 14 13 14 12

Annual vehicle kilometres

[km/a]

14,000 11,000 14,000 50,000 100,000

All-electric annual vehicle

kilometres [km/a]

14,000 11,000 7,000 50,000 50,000

Energy consumption

[kWh/100km]

20 16 18 120 140

Braking energy recovery in AS

[% fuel consumption]

20% 20% 20% 12% 6%

All-electric range [km] 320 200 50 240 86

Annual number of full cycles

[cycle]

120 120 120 300 600

Maximum DOD (stroke) [%] 80% 80% 75% 80% 75%

Typical capacity of the

application [kWh]

80 40 12 360 160

Min capacity of the application

[kWh]

60 20 4 170 n/a

Max capacity of the

application [kWh]

100 60 20 1,000 n/a

Battery calendar life (no

cycling) [a]

20 20 20 20 20

Battery cycle life (no calendar

aging) [FC]

1,500 1,500 2,000 2,000 3,000

Application Service Energy

(AS) [kWh]

96,000 48,000 18,000 576,000 360,000

Maximum quantity of

functional units (QFU) over

application service life

[kWh]

43,680 29,568 19,656 940,800 890,400

Battery energy efficiency 92% 92% 92% 92% 92%

Energy consumption due to

battery energy efficiency

[kWh]

7,680 3,840 1,440 46,080 28,800

Self-discharge rate

[%/month]

2% 2% 2% 2% 2%

Average SOC [%] 50% 50% 50% 50% 50%

Energy consumption due to

self-discharge [kWh]

192 96 29 864 384


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3.1.1.3.2. Base cases for stationary ESS

Rated energy

Referring to Graulich et al. (2018) and Figgener et al. (2018) residential ESS have an average

battery energy of approximately 10 kWh, although a range of 1 to 20 kWh is possible.

The battery energy and power of currently installed commercial ESS varies widely between 0.25 and 129 MWh (see Hornsdale Power Reserve (2018) and Task 2). For commercial ESS a trend towards bigger rated energies can be seen, thus a total application rated energy of 30,000 kWh is assumed.

Depth of Discharge

According to Stahl (2017) the DOD of residential and commercial ESS is at 90%. However,

that DOD is only relevant for some limited applications and thus, 80% are assumed.

Annual full cycles and calendar life base case

Batteries that are coupled with PV (residential ESS) are expected to be subject to 200 to 250

full cycles per year. The upper boundary is chosen for the base case, following expert

interviews. These figures are average values that might represent central Europe. Of course,

in Scandinavian countries these figures would be much lower, while in southern European

countries, such as Spain or Italy these figures would be higher.

It also has to be noted, that the number of cycles might increase in future, when these

residential ESS are allowed to provide grid services, such as primary frequency control on top

of self-consumption. In some EU member states the regulation is about to change, in order to

allow residential ESS to provide grid services. There is a lack of empirical data on how many

cycles would be added. According to experts 50 to 80 additional cycles per year are realistic,

which could be increased to up to one daily grid service cycle (365 annual cycles in total) for

revenue-optimising residential actors.

Thielmann et al. (2015b) state, that calendar life of a battery and the PV system should

coincide, which is 15 to 25 years for the latter. Thus, 25 years are assumed. Consequently,

less than 5,000 full cycles would be required. For German residential ESS Holsten (2018)

confirm on average around 400 full-load hours of use per year and thus 200 full cycles. Figure

14 shows a typical daily load profile of a residential PV system coupled with an ESS. The

battery is charged during daytime and the stored energy is consumed during the night. Further,

Holsten (2018) determine a figure of around 450 full-load hours per year for commercial ESS

which corresponds to 225 full cycles. However, we assume 250 cycles, since demand for

flexible ESS might increase in future due to the increasing share of renewable energy

generation. Figure 15 shows the load profile of a commercial ESS, depicting the high

fluctuations of feed-in and feed-out.

Cycle life and calendar life battery

For residential ESS a cycle life of the battery of 8,000 cycles and for commercial ESS of 10,000 cycles seems to be feasible (Holsten 2018), in combination with a calendar life of 25 years for residential and commercial ESS (expert interviews).

Energy efficiency

As in EV applications, an energy efficiency of 96% is assumed.

Since residential ESS are usually operated within private houses, ambient conditions are no critical issue and the operating temperature can be expected to be little under room temperature. The same applies to commercial ESS. Gravimetric and volumetric energy


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density are also only of minor relevance, because space and weight in private houses or commercial sites are not as limited as in EV for example (Thielmann et al. 2015b).

Figure 14: Household load profile of PV with and without battery (Source:(SMA 2014) SMA

(2014) )

Figure 15: Load profile of commercial ESS (source: Hornsdale Power Reserve (2018))


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The data discussed in the previous paragraphs is summed up in Table 2. We included application specific parameters such as lifetime, annual full cycles, DOD and typical range of the battery capacity in that application. Further, we calculated the quantity of functional units according to section 3.1.1.1 and the application service energy as well as energy consumption due to battery energy efficiency and due to self-discharge according to section 3.1.1.2.1 for each application. Those figures are related to the strict product approach.

Table 2: Summary of data required for the calculation of ESS base cases

Residential ESS Commercial ESS

Economic lifetime of the application [a] 20 20

Annual full cycles [FC/a] 250 250

Maximum DOD (stroke) [%] 80% 80%

Typical system capacity [kWh] 10 30,000

Minimum system capacity 2.5 250

Maximum system capacity 20 130,000

Battery calendar life (no cycling) [a] 25 25

Battery cycle life (no calendar aging) [FC] 8,000 10,000

Application service energy 40,000 120,000,000

Maximum quantity of functional units (QFU) over battery

service life 64,000 240,000,000

Battery system energy efficiency 92% 92%

Energy consumption due to battery energy efficiency [kWh] 5,120 19,200,000

Self-discharge rate [%/month] 2% 2%

Average SOC [%] 50% 50%

Energy consumption due to self-discharge [kWh] 30 90,000

3.1.1.3.3. Summary of standard test conditions for EV and ESS battery packs and systems

Two standards that are widely used for the testing of EV batteries are IEC 62660 and ISO

12405. While IEC 62660 refers to cells testing, ISO 12405 refers to systems testing (see

MAT4BAT 2016, EnergyVille 2019 and Annex to Task 1 “Analysis of available relevant

performance standards & methods in relation to Ecodesign Regulation for batteries and

identification of gaps” for further details). The standards related to EVs are depicted in Table

3, whereas the standard related to ESS is depicted in Table 4.

Table 3: Standard test conditions for EV (Source: based on MAT4BAT Advanced materials for

batteries (2016), EnergyVille (2019) and Annex to Task 1)

Test Application Test conditions IEC 62660-

1:2010

Test conditions ISO 12405-

4:2018

En

erg

y e

ffic

ien

cy BEV/PHEV

@100%, 70% SOC

@-20°C, 0°C, 25°C, 45°C

Charge according to the

manufacturer and rest 4 hours

discharge BEV @C/3, HEV @1C

@ 65%, 50%, 35% SOC

@ 0°C, 25°C, 40°C

12s charge pulse @Imax (or 20C)

and rest 40s then 16s discharge

pulse @0.75 Imax (or 15C)

BEV Fast charging

@25°C

Fast charging

@0°C, 25°C


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Charge @2C to 80% SOC and rest

4 hours

Charge @2C to 70% SOC and rest

4 hours

Charge @1C and rest 4 hours

Charge @2C and rest 4 hours

Charge @Imax and rest 4 hours

Self

-dis

ch

arg

e

BEV/PHEV Stored @45°C, conditioned @25°C

@50% SOC

Determination with 1C

Duration 28 days, checkup 28 days

BEV @25°C, 40°C

@100% SOC

No load for 48h, 168h, 720h

PHEV @25°C, 40°C

@80% SOC

No load for 24h, 168h, 720h

Cycle

lif

e

BEV/PHEV Stored @45°C, conditioned @25°C

@SOC window 100%-20% and

80%-25%,

Different BEV and HEV profiles

Check-up every 28 days at 25°C

End of test if C(current)


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Table 4: Standard test conditions for ESS (Source: Annex to Task 1 and IEC 2015)

Test Application IEC 61427-2: 2015 E

nerg

y

eff

icie

ncy

residential

and

commercial

ESS

Calculate average of:

@ RT, max and min ambient temperature during enduring test with

defined profile

Waste

heat

@ Max ambient temperature during endurance test with defined

profile

En

erg

y r

eq

uir

em

en

ts

du

rin

g id

le s

tate

@ RT during periods of idle state

Self

-dis

ch

arg

e

@ RT

@ 100% SOC for UPS, 50% SOC for other applications

1C

Check-up every 42 days, end after 3 repetitions

Serv

ice lif

e

@ RT - 40°C according to TMS

@SOC window 100%-20%

with endurance test profile

Check-up every 28 days at 25°C

3.1.2. Extended product approach

In chapter 3.1.1 we showed the importance of rated battery energy, depth of discharge or state

of charge respectively, battery energy efficiency, self-discharge, cycle life and calendar life but

also actual utilisation of batteries, stated as annual full cycles, on the direct energy

consumption of batteries.

By now, the impact of these parameters was discussed from a global perspective and mainly

in relation to technical standards. Thus, following the extended product approach, within this

chapter the actual utilisation of batteries under real-life conditions will be discussed. Further,

deviations of real-life utilisation from test standards are discussed.

Table 5 provides an overview of real-life deviations of EVs and ESS from standard test

conditions and how they are considered.


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Table 5: Real-life deviations from standard test conditions

Potential deviation from

standards

Explanation How it is considered

Driving profiles or load

profiles

Different load profiles of

batteries in urban, freeway

and highway traffic but also

in different regions, for

example, when used in grid

stabilization

Only considered via average

energy consumption

measured with a specific test

cycle

Only average cycles

considered

Driving patterns Different driving distances

and duration on weekdays/

at weekend; load profiles for

ESS vary over the years and

within a year

Average daily driving

distances and durations

assumed per base case

Charging strategy Charging C-rates, frequency

and duration vary

Standard charge strategy

defined for each base case

Temperature Ambient temperatures vary

(winter, summer, region,

etc., even daily)

TMS is expected to be

standard, thus not

considered

In general, the energy efficiency of a battery is influenced by load profiles

(charging/discharging and SOC ranges while being under load), which are directly linked to

driving profiles of electric vehicles or load profiles of stationary applications. Driving patterns

and load profiles influence no-load losses and the required annual full cycles. Furthermore

Preparatory Study on Ecodesign and Energy Labelling of ... · FWC ENER/C3/2015-619-Lot 1 TASK 3...

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